Emission images (Single-photon emission computerized tomography (SPECT) images or positron emission tomography (PET) images) of subjects injected with a tracer targeting the dopaminergic system (such as DaTscan® 123I-Ioflupane SPECT tracer sold by GE Healthcare of Arlington Heights, Ill.) have been either interpreted visually or by combining visual observation and semi-quantitative analysis. Quantification involves (manual or automatic) placement of regions of interest over the striatum and computing uptake ratios with respect to a background region, and the left-right asymmetries with respect to uptake. Quantification has been shown to increase inter-reader agreement and the reporting confidence, and to reduce the number of equivocal cases. Due to age-dependence of DaTscan® tracer uptake, comparisons to age-matched reference values are of most help in resolving challenging cases (e.g., borderline or early disease cases).
A convolutional neural network (CNN) can be trained to perform classification for Parkinsonian Syndromes (PSs). For example, Wu et al., “Deep Learning on 18F-FDG PET Imaging for Differential Diagnosis of Parkinsonian Syndromes,” J Nucl Med, vol. 59, No. supplement 1, p 624, May 1, 2018, describes a method to compress three-dimensional (3D) imaging data into two-dimensional (2D) data and use the 2D data to train CNNs for differential diagnosis of Parkinsonism using 18F-Fluorodeoxyglucose (18F-FDG) PET brain images.